2021 IEEE International Conference on Progress in Informatics and Computing (PIC)最新文献_第3页

Computer Vision for Astronomical Image Analysis 天文图像分析的计算机视觉

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687023

Sotiria Karypidou, Ilias Georgousis, G. Papakostas

{"title":"Computer Vision for Astronomical Image Analysis","authors":"Sotiria Karypidou, Ilias Georgousis, G. Papakostas","doi":"10.1109/PIC53636.2021.9687023","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687023","url":null,"abstract":"Computer Vision (CV) is undoubtedly one of the most popular forms of Artificial Intelligence (AI) and its implementation has gained considerable ground in all aspects of our lives, from security and automotive, to the night sky observation and astronomy. In general, CV uses pattern recognition techniques for identifying objects in visual media (both static and moving images). The current archetype in CV is largely based on supervised AI, which uses large data sets of human-labelled images for training. Machine Learning (ML) and Deep Learning (DL) models in computer vision have undergone a period of extremely rapid development in recent past years; in particular for object recognition and localisation tasks. An area of study with great interest in practical applications that concerns this essay, is astronomical images analysis. However, one of the main challenges facing researchers these days is the existence of large quantities of annotated data sets, in the appropriate resolution and scale. This challenge consequently asks for huge amounts of storage and high computational power. In this paper, we systematically review and analyze different challenges faced by astronomers and continue with state-of-the-art methodologies that were conducted over the last decade.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121028555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Random Noise Boxes: Data Augmentation for Spectrograms 随机噪声盒:频谱图的数据增强

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687058

Maxime Goubeaud, Nicolla Gmyrek, Farzin Ghorban, Lucas Schelkes, A. Kummert

引用次数: 2

Using Sparrow Search Hunting Mechanism to Improve Water Wave Algorithm 利用麻雀搜索机制改进水波算法

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687028

Haotian Li, Baohang Zhang, Jiayi Li, Tao Zheng, Haichuan Yang

引用次数: 9

Domain Adaptive Visual Tracking with Multi-scale Feature Fusion 基于多尺度特征融合的域自适应视觉跟踪

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687038

Qianqian Yu, Yi-Yang Wang, Keqi Fan, Y. Zheng

引用次数: 0

Toward More Effective Use of Assertions for Mobile App Development 在移动应用开发中更有效地使用断言

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687079

Yoonsik Cheon

引用次数: 1

Image Texture Removal by Total Variantional Rolling Guidance 基于全变分滚动制导的图像纹理去除

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687011

Wei Wang, Yi Yang, Xin Xu

引用次数: 0

CA-NCF: A Category Assisted Neural Collaborative Filtering Approach for Personalized Recommendation CA-NCF:一种分类辅助的个性化推荐神经协同过滤方法

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687049

Yimin Peng, Rong Hu, Yiping Wen

{"title":"CA-NCF: A Category Assisted Neural Collaborative Filtering Approach for Personalized Recommendation","authors":"Yimin Peng, Rong Hu, Yiping Wen","doi":"10.1109/PIC53636.2021.9687049","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687049","url":null,"abstract":"In the big data environment, the sparsity problem of collaborative filtering recommendation algorithm becomes increasingly serious, which has a great impact on the accuracy of recommendation. In some recent researches, item categories were input into neural networks to enrich the embedded information in the process of training. However, these methods generally simultaneously use item categories and items as embedded information, which may weaken the importance of item categories. Therefore, this paper proposes a neural collaborative filtering method based on category assistance. In this method, the interaction between item category and user is first modeled by Neural Matrix Factorization ((Neu-MF)), which raises the impact of item category in the relationship extraction between items and users. Then, only the items in the trained results of categories are used in an optimized Neural Collaborative Filtering (NCF) framework for item recommendation. Based on the real ecommerce data set from Alibaba, experimental results show that this method obtains better result in the Hit Rate (HR) and the Normalized Discounted Cumulative Gain (NDCG) compared with other baseline methods.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134497971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Double-Sided Auction Mechanism for Peer-to-Peer Energy Trading Markets 点对点能源交易市场的双边拍卖机制

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687026

Jema Sharin PankiRaj, A. Yassine, Salimur Choudhury

{"title":"Double-Sided Auction Mechanism for Peer-to-Peer Energy Trading Markets","authors":"Jema Sharin PankiRaj, A. Yassine, Salimur Choudhury","doi":"10.1109/PIC53636.2021.9687026","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687026","url":null,"abstract":"The emerging smart grid uniquely combines two-way communication and energy flow, allowing consumers to become active participants in market-based energy supply and demand strategies. In such a market, Peer-to-Peer (P2P) energy trading paradigm allows local communities and individuals who generate electricity to freely decide how and with whom they are going to trade it. The greatest challenge of P2P energy trading is how to design efficient mechanisms among rational participants that maximize their monetary benefits. Furthermore, since utility companies own the transmission lines, a key question that yet to be addressed in P2P markets is: how to match between different energy buyers and sellers while taking into account the physical constraints of the underlying grid infrastructure, e.g., capacity, congestion, and line transmission costs. This paper proposes a novel double-sided auction mechanism with a matching algorithm that addresses the aforementioned challenges. In this paper, the social welfare of the participants is modeled as an optimization problem with cost constraints incurred due to energy generation, operating and maintenance, capacity, and line transmission costs. The study provides theoretical analysis of the P2P auction model including mechanism design properties such as individual rationality, computational efficiency, and truthfulness. The results of the experiments indicate that the proposed auction model outperform existing systems and yields better economic incentives for participants.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128419960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Weighted Dependence of the Day of the Week in Patients with Emotional Disorders: A Mathematical Model 情绪障碍患者星期几的加权依赖:一个数学模型

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687019

Pavél Llamocca Portella, Victoria López, Matilde Santos

{"title":"Weighted Dependence of the Day of the Week in Patients with Emotional Disorders: A Mathematical Model","authors":"Pavél Llamocca Portella, Victoria López, Matilde Santos","doi":"10.1109/PIC53636.2021.9687019","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687019","url":null,"abstract":"People who suffer from depression or bipolar disorder have very different and complex indicators of their emotional state. The use of wearable smart devices can help to characterize the behaviour of these people and therefore allows the psychiatrist to decide the best treatment. In addition, those devices are able to extract a great amount of data from patients that can be analyzed with computer techniques. However, most patients experience fluctuations in mood according to a weekly cycle. The day of the week is a factor that influences a set of characteristics that describe the emotional state, like irritability or motivation. In this work, we analyze this factor and its influence on a set of mood variables gathered daily and their relation with the medical diagnostic of the patient. The analysis of the information is personalized since the data presents variations due to factors that affect the emotional state of each patient according to different ways and intensities. This work presents an improved mathematical model on the diagnosis by including the factor described before.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127557613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Single Image Based Depth Estimation for Maritime Surface Targets 基于单图像的海面目标深度估计

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687055

Jishan Sun, Yaojie Chen, Wei Wang

引用次数: 0